{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Scrape top 250 movies from Douban\n",
    "# scrapy crawl douban250 -o douban250.json -t json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>director</th>\n",
       "      <th>movie_name</th>\n",
       "      <th>ranking</th>\n",
       "      <th>score</th>\n",
       "      <th>score_num</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>弗兰克·德拉邦特</td>\n",
       "      <td>肖申克的救赎</td>\n",
       "      <td>1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>1760901</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>陈凯歌</td>\n",
       "      <td>霸王别姬</td>\n",
       "      <td>2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1300863</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>罗伯特·泽米吉斯</td>\n",
       "      <td>阿甘正传</td>\n",
       "      <td>3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1357029</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>吕克·贝松</td>\n",
       "      <td>这个杀手不太冷</td>\n",
       "      <td>4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1551974</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>罗伯托·贝尼尼</td>\n",
       "      <td>美丽人生</td>\n",
       "      <td>5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>859148</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>詹姆斯·卡梅隆</td>\n",
       "      <td>泰坦尼克号</td>\n",
       "      <td>6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1295144</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>宫崎骏</td>\n",
       "      <td>千与千寻</td>\n",
       "      <td>7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1386873</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>史蒂文·斯皮尔伯格</td>\n",
       "      <td>辛德勒的名单</td>\n",
       "      <td>8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>698070</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>克里斯托弗·诺兰</td>\n",
       "      <td>盗梦空间</td>\n",
       "      <td>9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1320824</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>莱塞·霍尔斯道姆</td>\n",
       "      <td>忠犬八公的故事</td>\n",
       "      <td>10</td>\n",
       "      <td>9.3</td>\n",
       "      <td>896728</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>朱塞佩·托纳多雷</td>\n",
       "      <td>海上钢琴师</td>\n",
       "      <td>11</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1083659</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>安德鲁·斯坦顿</td>\n",
       "      <td>机器人总动员</td>\n",
       "      <td>12</td>\n",
       "      <td>9.3</td>\n",
       "      <td>873246</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>拉库马·希拉尼</td>\n",
       "      <td>三傻大闹宝莱坞</td>\n",
       "      <td>13</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1201236</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>彼得·威尔</td>\n",
       "      <td>楚门的世界</td>\n",
       "      <td>14</td>\n",
       "      <td>9.3</td>\n",
       "      <td>947362</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>克里斯托夫·巴拉蒂</td>\n",
       "      <td>放牛班的春天</td>\n",
       "      <td>15</td>\n",
       "      <td>9.3</td>\n",
       "      <td>838997</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     director movie_name  ranking  score  score_num  year\n",
       "0    弗兰克·德拉邦特     肖申克的救赎        1    9.7    1760901  1994\n",
       "1         陈凯歌       霸王别姬        2    9.6    1300863  1993\n",
       "2    罗伯特·泽米吉斯       阿甘正传        3    9.5    1357029  1994\n",
       "3       吕克·贝松    这个杀手不太冷        4    9.4    1551974  1994\n",
       "4     罗伯托·贝尼尼       美丽人生        5    9.5     859148  1997\n",
       "5     詹姆斯·卡梅隆      泰坦尼克号        6    9.4    1295144  1997\n",
       "6         宫崎骏       千与千寻        7    9.3    1386873  2001\n",
       "7   史蒂文·斯皮尔伯格     辛德勒的名单        8    9.5     698070  1993\n",
       "8    克里斯托弗·诺兰       盗梦空间        9    9.3    1320824  2010\n",
       "9    莱塞·霍尔斯道姆    忠犬八公的故事       10    9.3     896728  2009\n",
       "10   朱塞佩·托纳多雷      海上钢琴师       11    9.3    1083659  1998\n",
       "11    安德鲁·斯坦顿     机器人总动员       12    9.3     873246  2008\n",
       "12    拉库马·希拉尼    三傻大闹宝莱坞       13    9.2    1201236  2009\n",
       "13      彼得·威尔      楚门的世界       14    9.3     947362  1998\n",
       "14  克里斯托夫·巴拉蒂     放牛班的春天       15    9.3     838997  2004"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "douban250 = pd.read_json('./douban/douban250.json')\n",
    "douban250[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ffa908f3710>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(douban250['score'], douban250['score_num'], s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Scrape watched movies from Meander's library\n",
    "# scrapy crawl douban_person -a person_id=184108788 -o meander.json -t json\n",
    "# scrapy crawl douban_person -a person_id=191384208 -o enzo.json -t json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alias</th>\n",
       "      <th>comment</th>\n",
       "      <th>date</th>\n",
       "      <th>director</th>\n",
       "      <th>movie_name</th>\n",
       "      <th>score</th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BEAK AND BRAIN: GENIUS BIRDS FROM DOWN UNDER</td>\n",
       "      <td>看着很享受，完全被啄羊鹦鹉圈粉了！</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>喙与脑：使用工具的鸟类</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://movie.douban.com/subject/26996797/</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bird Brain</td>\n",
       "      <td>可以配合《鸟类的天赋》一起看</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>揭秘鸟类大脑</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://movie.douban.com/subject/26125868/</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Finding Vivian Maier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>寻找薇薇安·迈尔</td>\n",
       "      <td>3.0</td>\n",
       "      <td>https://movie.douban.com/subject/21356379/</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Memento</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>记忆碎片</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/1304447/</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Whiplash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>爆裂鼓手</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://movie.douban.com/subject/25773932/</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Love Actually</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>真爱至上</td>\n",
       "      <td>3.0</td>\n",
       "      <td>https://movie.douban.com/subject/1292401/</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>The Croods</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>疯狂原始人</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/1907966/</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pride &amp; Prejudice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>傲慢与偏见</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/1418200/</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>歲月神偷</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>岁月神偷</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/3792799/</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>リトル・フォレスト 冬・春</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>小森林 冬春篇</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/25814707/</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>告白</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>告白</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://movie.douban.com/subject/4268598/</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>リトル・フォレスト 夏・秋</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>小森林 夏秋篇</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/25814705/</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>嫌われ松子の一生</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>被嫌弃的松子的一生</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/1787291/</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>低俗小说</td>\n",
       "      <td>3.0</td>\n",
       "      <td>https://movie.douban.com/subject/1291832/</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Love Letter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>情书</td>\n",
       "      <td>3.0</td>\n",
       "      <td>https://movie.douban.com/subject/1292220/</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>V for Vendetta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>V字仇杀队</td>\n",
       "      <td>3.0</td>\n",
       "      <td>https://movie.douban.com/subject/1309046/</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dangal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>摔跤吧！爸爸</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/26387939/</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Zootopia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>疯狂动物城</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/25662329/</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>となりのトトロ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>龙猫</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/1291560/</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>千と千尋の神隠し</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>千与千寻</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://movie.douban.com/subject/1291561/</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ハチ公物語</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-06-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>忠犬八公物语</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://movie.douban.com/subject/1959195/</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rick and Morty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>瑞克和莫蒂 第一季</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://movie.douban.com/subject/11537954/</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>君の膵臓をたべたい</td>\n",
       "      <td>讲故事没有轻重，有些地方看得我尴尬犯了</td>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>我想吃掉你的胰脏</td>\n",
       "      <td>3.0</td>\n",
       "      <td>https://movie.douban.com/subject/27107140/</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Hachi: A Dog's Tale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>忠犬八公的故事</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://movie.douban.com/subject/3011091/</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Life of Pi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>少年派的奇幻漂流</td>\n",
       "      <td>4.0</td>\n",
       "      <td>https://movie.douban.com/subject/1929463/</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           alias              comment  \\\n",
       "0   BEAK AND BRAIN: GENIUS BIRDS FROM DOWN UNDER    看着很享受，完全被啄羊鹦鹉圈粉了！   \n",
       "1                                     Bird Brain       可以配合《鸟类的天赋》一起看   \n",
       "2                           Finding Vivian Maier                  NaN   \n",
       "3                                        Memento                  NaN   \n",
       "4                                       Whiplash                  NaN   \n",
       "5                                  Love Actually                  NaN   \n",
       "6                                     The Croods                  NaN   \n",
       "7                              Pride & Prejudice                  NaN   \n",
       "8                                           歲月神偷                  NaN   \n",
       "9                                  リトル・フォレスト 冬・春                  NaN   \n",
       "10                                            告白                  NaN   \n",
       "11                                 リトル・フォレスト 夏・秋                  NaN   \n",
       "12                                      嫌われ松子の一生                  NaN   \n",
       "13                                  Pulp Fiction                  NaN   \n",
       "14                                   Love Letter                  NaN   \n",
       "15                                V for Vendetta                  NaN   \n",
       "16                                        Dangal                  NaN   \n",
       "17                                      Zootopia                  NaN   \n",
       "18                                       となりのトトロ                  NaN   \n",
       "19                                      千と千尋の神隠し                  NaN   \n",
       "20                                         ハチ公物語                  NaN   \n",
       "21                                Rick and Morty                  NaN   \n",
       "22                                     君の膵臓をたべたい  讲故事没有轻重，有些地方看得我尴尬犯了   \n",
       "23                           Hachi: A Dog's Tale                  NaN   \n",
       "24                                    Life of Pi                  NaN   \n",
       "\n",
       "         date  director   movie_name  score  \\\n",
       "0  2020-01-18       NaN  喙与脑：使用工具的鸟类    5.0   \n",
       "1  2020-01-18       NaN       揭秘鸟类大脑    5.0   \n",
       "2  2020-01-14       NaN     寻找薇薇安·迈尔    3.0   \n",
       "3  2020-01-11       NaN         记忆碎片    4.0   \n",
       "4  2020-01-11       NaN         爆裂鼓手    5.0   \n",
       "5  2020-01-11       NaN         真爱至上    3.0   \n",
       "6  2020-01-11       NaN        疯狂原始人    4.0   \n",
       "7  2020-01-11       NaN        傲慢与偏见    4.0   \n",
       "8  2020-01-11       NaN         岁月神偷    4.0   \n",
       "9  2020-01-11       NaN      小森林 冬春篇    4.0   \n",
       "10 2020-01-11       NaN           告白    5.0   \n",
       "11 2020-01-11       NaN      小森林 夏秋篇    4.0   \n",
       "12 2020-01-11       NaN    被嫌弃的松子的一生    4.0   \n",
       "13 2020-01-11       NaN         低俗小说    3.0   \n",
       "14 2020-01-11       NaN           情书    3.0   \n",
       "15 2020-01-11       NaN        V字仇杀队    3.0   \n",
       "16 2020-01-11       NaN       摔跤吧！爸爸    4.0   \n",
       "17 2020-01-11       NaN        疯狂动物城    4.0   \n",
       "18 2020-01-11       NaN           龙猫    4.0   \n",
       "19 2019-06-28       NaN         千与千寻    5.0   \n",
       "20 2019-06-08       NaN       忠犬八公物语    5.0   \n",
       "21 2019-03-25       NaN    瑞克和莫蒂 第一季    5.0   \n",
       "22 2019-01-28       NaN     我想吃掉你的胰脏    3.0   \n",
       "23 2019-01-17       NaN      忠犬八公的故事    5.0   \n",
       "24 2019-01-15       NaN     少年派的奇幻漂流    4.0   \n",
       "\n",
       "                                           url  year  \n",
       "0   https://movie.douban.com/subject/26996797/  2013  \n",
       "1   https://movie.douban.com/subject/26125868/     0  \n",
       "2   https://movie.douban.com/subject/21356379/  2013  \n",
       "3    https://movie.douban.com/subject/1304447/  2000  \n",
       "4   https://movie.douban.com/subject/25773932/  2014  \n",
       "5    https://movie.douban.com/subject/1292401/  2003  \n",
       "6    https://movie.douban.com/subject/1907966/  2013  \n",
       "7    https://movie.douban.com/subject/1418200/  2005  \n",
       "8    https://movie.douban.com/subject/3792799/  2010  \n",
       "9   https://movie.douban.com/subject/25814707/  2015  \n",
       "10   https://movie.douban.com/subject/4268598/  2010  \n",
       "11  https://movie.douban.com/subject/25814705/  2014  \n",
       "12   https://movie.douban.com/subject/1787291/  2006  \n",
       "13   https://movie.douban.com/subject/1291832/  1994  \n",
       "14   https://movie.douban.com/subject/1292220/  1995  \n",
       "15   https://movie.douban.com/subject/1309046/  2005  \n",
       "16  https://movie.douban.com/subject/26387939/  2016  \n",
       "17  https://movie.douban.com/subject/25662329/  2016  \n",
       "18   https://movie.douban.com/subject/1291560/  1988  \n",
       "19   https://movie.douban.com/subject/1291561/  2001  \n",
       "20   https://movie.douban.com/subject/1959195/  1987  \n",
       "21  https://movie.douban.com/subject/11537954/  2013  \n",
       "22  https://movie.douban.com/subject/27107140/  2018  \n",
       "23   https://movie.douban.com/subject/3011091/  2009  \n",
       "24   https://movie.douban.com/subject/1929463/  2012  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meander = pd.read_csv('./douban/meander.csv')\n",
    "meander['date'] = pd.to_datetime(meander['date'])\n",
    "meander[:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do some analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffaf0c09dd8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = meander.groupby(by=[meander[\"date\"].dt.year, meander[\"date\"].dt.month]).count()['date']\n",
    "sco = meander.groupby(by=[meander[\"date\"].dt.year, meander[\"date\"].dt.month])['score'].mean()\n",
    "res.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = meander.groupby([meander[\"date\"].dt.year, meander[\"date\"].dt.month]).count()['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "score\n",
       "3.0     6\n",
       "4.0    20\n",
       "5.0    12\n",
       "Name: score, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meander['score'].groupby(meander['score']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
